AI Engineer

Hitachi eBworx; Selangor; Hybrid; MYR 60,000 - 90,000; Full time

Job summary

A leading tech company in Malaysia is seeking an experienced AI RAG Engineer to design and optimize Retrieval-Augmented Generation (RAG) pipelines. The role involves integrating large language models with various data sources and enhancing knowledge retrieval systems. Ideal candidates should have expertise in LLM fine-tuning, reinforcement learning, and solid programming skills in Python. This opportunity promises career growth with competitive compensation and advanced technologies.

Benefits

Competitive salary

Career growth

Access to latest tools & tech

Qualifications

• Experience with LLM fine‑tuning and training is essential.
• Strong understanding of Transformer models and NLP architectures is required.
• Hands‑on experience with RLHF (Reinforcement Learning from Human Feedback) is a must.

Responsibilities

• Develop and optimize RAG pipelines for AI search and retrieval.
• Implement retrieval models using vector databases.
• Integrate structured and unstructured knowledge bases with AI systems.

Skills

LLM fine-tuning

Machine learning

NLP architectures

Reinforcement learning

Vector embeddings

Tools

Python

PyTorch

TensorFlow

SQL

NoSQL

Vector databases

Job description

We are seeking an experienced AI RAG Engineer to design and optimize Retrieval-Augmented Generation (RAG) pipelines, leveraging cutting‑edge AI and machine learning techniques. In this role, you will work on integrating large language models (LLMs) with structured and unstructured data sources, fine‑tuning AI models, and improving knowledge retrieval for enhanced accuracy and efficiency.

This is an exciting opportunity to be at the forefront of AI‑driven search and retrieval, working with some of the latest advancements in NLP, reinforcement learning, and scalable AI infrastructure.

What Awaits You

• Latest Tools & Tech – Work with cutting‑edge technologies to stay ahead.
• Career Growth – Access training programs for upskilling or reskilling to build your portfolio.
• Great Pay & Perks – Competitive salary and bonuses to reward your expertise and contributions.

What You’ll Do

• Develop and optimize RAG pipelines to enhance AI‑driven search and retrieval systems.
• Implement retrieval models using vector databases such as FAISS, Pinecone, Weaviate, and Milvus.
• Fine‑tune LLMs using supervised learning and reinforcement learning techniques like RLHF (Reinforcement Learning from Human Feedback).
• Design and train embedding models to improve document retrieval and knowledge extraction.
• Integrate structured and unstructured knowledge bases with AI systems to enhance response relevance.
• Improve response accuracy and contextual coherence in AI‑generated outputs.
• Develop and optimize retrieval and ranking algorithms for real‑time applications.
• Leverage open‑source AI models (e.g., Llama, Mistral, GPT, Claude) to enhance retrieval efficiency.
• Optimize AI model deployment and improve training pipelines for production environments.

What We Need From You

Core AI & ML Skills

• Experience with LLM fine‑tuning and training (e.g., Hugging Face, OpenAI API, LangChain).
• Strong understanding of Transformer models and NLP architectures.
• Knowledge of vector embeddings, semantic search, and retrieval models (BM25, DPR, ColBERT, Hybrid Search).
• Expertise in reinforcement learning and self‑supervised learning.
• Hands‑on experience with RLHF (Reinforcement Learning from Human Feedback).

Programming & Frameworks

• Proficiency in Python, and experience with PyTorch, TensorFlow, JAX.
• Experience with LLM orchestration frameworks (LangChain, LlamaIndex).
• Strong skills in SQL and NoSQL databases for knowledge storage.
• Experience with API integrations for AI systems (OpenAI, Anthropic, Hugging Face).
• Familiarity with distributed computing and ML model scaling.
• Experience with vector databases (FAISS, Pinecone, Milvus, Weaviate).
• Strong understanding of ETL pipelines for processing large‑scale datasets.
• Experience deploying AI models in cloud environments (AWS, Azure, GCP).
• Knowledge of containerization (Docker, Kubernetes) and MLOps practices.